This paper presents a model for partitioning two modes of three-way proximity data which generalizes INDCLUS by incorporating possible external information on objects and/or subjects. Specifically, subjects are partitioned into homogeneous classes, where class-conditional groups of objects are determined. The classifications of both objects and subjects are assumed to be related to possible external variables to better account for the meaning and the determinant of the groups. The model is fitted in a least-squares framework and an efficient ALS algorithm is given. An illustrative application to a benchmark data set is presented.

This paper presents a model for partitioning two modes of three-way proximity data which generalizes INDCLUS by incorporating possible external information on objects and/or subjects. Specifically, subjects are partitioned into homogeneous classes, where class-conditional groups of objects are determined. The classifications of both objects and subjects are assumed to be related to possible external variables to better account for the meaning and the determinant of the groups. The model is fitted in a least-squares framework and an efficient ALS algorithm is given. An illustrative application to a benchmark data set is presented.

A general model for INDCLUS with external information / Bocci, Laura; Vicari, Donatella. - STAMPA. - (2013), pp. 53-56. (Intervento presentato al convegno Cladag 2013 tenutosi a Modena, Italia nel 12-20 Settembre 2013).

A general model for INDCLUS with external information

BOCCI, Laura;VICARI, Donatella
2013

Abstract

This paper presents a model for partitioning two modes of three-way proximity data which generalizes INDCLUS by incorporating possible external information on objects and/or subjects. Specifically, subjects are partitioned into homogeneous classes, where class-conditional groups of objects are determined. The classifications of both objects and subjects are assumed to be related to possible external variables to better account for the meaning and the determinant of the groups. The model is fitted in a least-squares framework and an efficient ALS algorithm is given. An illustrative application to a benchmark data set is presented.
2013
Cladag 2013
This paper presents a model for partitioning two modes of three-way proximity data which generalizes INDCLUS by incorporating possible external information on objects and/or subjects. Specifically, subjects are partitioned into homogeneous classes, where class-conditional groups of objects are determined. The classifications of both objects and subjects are assumed to be related to possible external variables to better account for the meaning and the determinant of the groups. The model is fitted in a least-squares framework and an efficient ALS algorithm is given. An illustrative application to a benchmark data set is presented.
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A general model for INDCLUS with external information / Bocci, Laura; Vicari, Donatella. - STAMPA. - (2013), pp. 53-56. (Intervento presentato al convegno Cladag 2013 tenutosi a Modena, Italia nel 12-20 Settembre 2013).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/530809
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